<![CDATA[Data mining is a process to support decision making in finding information patterns in the data. In this study, Association Rule Mining will be implemented as one of the data mining techniques to analyze data and assist data of scientists in compiling raw data, formulating it and recognizing various patterns through a priori algorithms. The method used in this study is the Cross Industry Standard Process for Data Mining (CRISP-DM) Method by analyzing drug use patterns in health centers. The results of the study shows that by using the apriori algorithm, it found patterns and rules of widely used drugs that will provide recommendations in supporting decision making by health centers to submit drug procurement so that they can improve the quality of health services and minimize the risk of shortages or excess drug supplies and help health centers in optimizing drug inventory management. The results of the analysis using the apriori algorithm on the combination pattern of 2 itemsets produced 2 association rules for drug use, they are "If using Amoxicillin caplets 500 mg, then you will use paracetamol" with a confidence value of 80% and "If using Dexamethasone tablets 0.5 mg, then you will use Ascorbic Acid (Vit C) tablets 50 mg" with a confidence value of 100%.]]>
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